In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

This dataset holds all materials for the Inform E-learning GIS course

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

The Hills of Governor's Island Dataset for GRASS GIS
This geospatial dataset contains raster and vector data for the Hills region of Governor's Island, New York City, USA. The top level directory governors_island_hills_for_grass is a GRASS GIS location for NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet in US Surveyor's Feet with EPSG code 2263. Inside the location there is the PERMANENT mapset, a license file, data record, readme file, workspace, color table, category rules, and scripts for data processing. This dataset was created for the course GIS for Designers.

Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database
directory. If you are new to GRASS GIS read the first time users guide.

Data Sources

• https://orthos.dhses.ny.gov/
• https://data.cityofnewyork.us/

Maps

• Orthophotographs from 2012, 2014, 2016, 2018, and 2020
• Digital elevation model from 2017
• Digital surface models from 2014 and 2017
• Landcover from 2014

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

The Unpublished Digital Geologic-GIS Map of Parts of Great Sand Dunes National Park and Preserve (Sangre de Cristo Mountains and part of the Dunes), Colorado is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (gsam_geology.gdb), a 10.1 ArcMap (.mxd) map document (gsam_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (grsa_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (grsa_geology_gis_readme.pdf). Please read the grsa_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (gsam_geology_metadata.txt or gsam_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 13N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Great Sand Dunes National Park and Preserve.

Stave off the garbage-in-garbage-out scenario and learn how to maintain authoritative geographic data. The courses and resources below will help you build the skills needed to store, organize, update, and disseminate accurate data that supports sound decision-making.Goals Create a geodatabase to organize and manage geographic data. Deploy recommended editing workflows to update 2D and 3D data. Apply ArcGIS best practices to maintain the accuracy of geographic data over time.

One important reason for performing GIS analysis is to determine proximity. Often, this type of analysis is done using vector data and possibly the Buffer or Near tools. In this course, you will learn how to calculate distance using raster datasets as inputs in order to assign cells a value based on distance to the nearest source (e.g., city, campground). You will also learn how to allocate cells to a particular source and to determine the compass direction from a cell in a raster to a source.What if you don't want to just measure the straight line from one place to another? What if you need to determine the best route to a destination, taking speed limits, slope, terrain, and road conditions into consideration? In cases like this, you could use the cost distance tools in order to assign a cost (such as time) to each raster cell based on factors like slope and speed limit. From these calculations, you could create a least-cost path from one place to another. Because these tools account for variables that could affect travel, they can help you determine that the shortest path may not always be the best path.After completing this course, you will be able to:Create straight-line distance, direction, and allocation surfaces.Determine when to use Euclidean and weighted distance tools.Perform a least-cost path analysis.

The New Zealand version of the Zombie Apocalypse lesson."A huge yet unknown catastrophic event has changed the world as we know it. One of the major results of this event is the spread of zombies across the globe. People all around the world are trying to survive this zombie invasion the best way they can. It will take skill, and of course BRAINS to figure out the best way to survive this catastrophe!
Do you have what it takes? Can you use what is in YOUR BRAIN to SURVIVE the ZOMBIE APOCALYPSE!?"

Active non-wholesale Water and Provisional Water Utility Service Areas as listed in the Regulatory Commission of Alaska (RCA) Certificate details for regulated utilities. Likely the most comprehensive collection of State of Alaska utility service areas - but not necessarily definitive for every utility. For complicated large city service areas such as water and sewer the GIS department that represents those cities might have the best representation of the service area. There are also utilities that may not be regulated by RCA which will not be in the data. Footprints in general were lifted from existing KML files created by a contractor in the years 2008-2017. Service area changes that have happened since 2008 may not yet be reflected in the footprints. In a few cases legal descriptions had typos which resulted in service areas miles from the community they intended to cover. In the case of the AsOfDate attribute in this dataset only reflects the date of the last syncing of master certificate metadata with RCA Library database - not the current polygon representation.Source: Regulatory Commission of AlaskaThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Regulatory Commission of Alaska Library

This is series-level metadata for the USGS Protected Areas Database of the United States (PAD-US) data released by the United States Geological Survey (USGS). PAD-US is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas. Starting with version 1.4 PAD-US was identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://ngda-cadastre-geoplatform.hub.arcgis.com/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling "best available" data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/programs/gap-analysis-project/science/protected-areas/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-history for more information): - Current Version - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B - Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT - Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE - Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ - Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD - Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov) - Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov) - First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov) Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu.We captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024.Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each area.The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857.For using these data:- The Adobe Suite gives you great software to open .Tif files.- You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.- Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk.- You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files.- The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file.This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

Update date: from GISP repository on 2/6/25. This is a static dataset.Data Type: polyline dataAn open channel is digitized from paper or scanned imagery.Subtypes:Improved: An open drainage course confined with lined or unlined embankments.Unimproved: A natural drainage course graded to channelize storm water.Swale: A graded depression with relatively low slope to channelize storm water. Ditch: A trench provided to channelize storm water. Attributes: Most of the feature classes in this storm drain geometric network share the same GIS table schema. Only the most critical attributes per operations of the Los Angeles County Flood Control District are listed below:AttributeDescriptionASBDATEThe date the design plans were approved "as-built" or accepted as "final records".CROSS_SECTION_SHAPEThe cross-sectional shape of the pipe or channel. Examples include round, square, trapezoidal, arch, etc.DIAMETER_HEIGHTThe diameter of a round pipe or the height of an underground box or open channel.DWGNODrain Plan Drawing Number per LACFCD NomenclatureEQNUMAsset No. assigned by the Department of Public Works (in Maximo Database).MAINTAINED_BYIdentifies, to the best of LAFCD's knowledge, the agency responsible for maintaining the structure.MOD_DATEDate the GIS features were last modified.NAMEName of the individual drainage infrastructure.OWNERAgency that owns the drainage infrastructure in question.Q_DESIGNThe peak storm water runoff used for the design of the drainage infrastructure.SOFT_BOTTOMFor open channels, indicates whether the channel invert is in its natural state (not lined).SUBTYPEMost feature classes in this drainage geometric nature contain multiple subtypes. 1 = Improved, 2 = Unimproved, 3 = Ditch, 4 = SwaleUPDATED_BYThe person who last updated the GIS feature.WIDTHWidth of a channel in feet.This Storm Drain Dataset is a work in progress, and all users of this data are STRONGLY ENCOURAGED to obtain the most current copy, available for download at the LA County eGIS Hub site.Terms of UseThis data is derived from the County Cadastral Landbase and features are typically added to this dataset per recorded 'as-built' drawings. Accurate facility locations on the ground must be determined by qualified field personnel. If any errors are found, or if there are general questions, please contact the individuals listed in the Credits.This product is for information purposes and should not be used for legal, engineering, or survey purposes. County assumes no liability for any errors or omissions.

CanVec contains more than 60 topographic features classes organized into 8 themes: Transport Features, Administrative Features, Hydro Features, Land Features, Manmade Features, Elevation Features, Resource Management Features and Toponymic Features. This multiscale product originates from the best available geospatial data sources covering Canadian territory. It offers quality topographic information in vector format complying with international geomatics standards. CanVec can be used in Web Map Services (WMS) and geographic information systems (GIS) applications and used to produce thematic maps. Because of its many attributes, CanVec allows for extensive spatial analysis. Related Products: Constructions and Land Use in Canada - CanVec Series - Manmade Features Lakes, Rivers and Glaciers in Canada - CanVec Series - Hydrographic Features Administrative Boundaries in Canada - CanVec Series - Administrative Features Mines, Energy and Communication Networks in Canada - CanVec Series - Resources Management Features Wooded Areas, Saturated Soils and Landscape in Canada - CanVec Series - Land Features Transport Networks in Canada - CanVec Series - Transport Features Elevation in Canada - CanVec Series - Elevation Features Map Labels - CanVec Series - Toponymic Features

EGLE administers the statewide Michigan Green Schools certification program. The program is dedicated to assisting all Michigan schools public and private achieve environmental goals that include protecting the air, land, water and animals of our state along with world outreach through good ecological practices and the teaching of educational stewardship of students pre-kindergarten through high school.A school is eligible to receive a Green School, Emerald School, or Evergreen School Environmental Stewardship Designation if the school or students perform the required number of activities, with a minimum of two activities from each of the four categories. The activity requirements for each level of environmental stewardship designation are as follows:Fields included in this dataset are:SchoolName: The name of the school.SchoolCity: The city that the school is in.SchoolCounty: The county that the school is in.CountyCoordName: The name of the county coordinator that approved the schoolCertificationLevel: The Green Schools certification level achieved based on number of activities achieved.Green: 10 total activities with at least two activities from each of the four categories.Emerald: 15 total activities with at least two activities from each of the four categories.Evergreen: 20 total activities with at least two activities from each of the four categories.Awaiting Final Result: Macomb County has not sent the final certification levels to the State of Michigan.Please visit EGLE's Green School site for more information and direct questions to Eileen Boekestein, EGLE's Environmental Education Coordinator at BoekesteinE@Michigan.gov.

Didemnum vexillum is an invasive sea squirt that is not native to UK shores. It was first detected in Europe in 1991 and has since spread to several countries (including France, Ireland and the UK). The species has been located in Wales, Scotland and England and there is concern D. vexillum may have negative impacts on biodiversity and shellfish interests.

Predicting the spread of an invasive species is crucial when assessing possible management actions. The potential impacts of the species on both biodiversity and commercial interests need to be studied and a cost-benefit approach taken to decide on the best course of management for that species.

Geographic Information System (GIS) offers a fast, efficient way to map this predicted spread. The results of this mapping can then be used to focus on areas where D. vexillum may conflict with conservation and commercial interests.

The dataset shows the locations where eelgrass was observed during the 2022 field season within the following water bodies: Great Bay, the mouth of the Squamscott River, the tidal portion of the Lamprey River, the tidal portion of the Oyster River, the tidal portion of the Bellamy River, the Piscataqua River and Portsmouth Harbor and its associated creeks, and the coastal shore of Odiorne Point. Photointerpretation was completed on orthophotography collected using a UAV over the study area. Sentinel satellite imagery was also used to further complement these datasets to help with photointerpretation. GoPro camera aerial imagery was also taken from a small plane over the whole of the study to further complement the photointerpretation of the imagery. UAV imagery was collected as five spectral bands inclusive of RGB, Red-Edge ,and Near Infrared bands. Imagery was collected with varying 1.7 to 3.4 cm spatial resolutions based on 100 to 400 ft flight altitudes specific to FAA maximum flight restrictions around the Pease International Tradeport. The Sentinel satellite imagery was acquired for the date of August 2nd, 2022 with a nominal 10-meter spatial resolution. The GoPro imagery captured non-rectified true-color photographs. Environmental conditions including low-sun angles, low tides, low cloud cover, calm winds, no preceding rain events, and low turbidity in the water column, were maintained throughout the course of the UAV survey when possible. The eelgrass habitat mapped from the imagery was verified using the ground truthing data from preselected locations and ad hoc locations chosen during the course of the field work. Ground truthing was done from a small boat during the same season as the photographs were taken. Ground truth observations were made on all tides using a drop camera with video recording capabilities and at low tide when visual observations were recorded.

Farmland information was obtained from the Farmland Mapping & Monitoring Program (FMMP) in the Division of Land Resource Protection in the California Department of Conservation. Established in 1982, the FMMP is to provide consistent and impartial data and analysis of agricultural land use and land use changes throughout the State of California. The study area is in accordance to the soil survey developed by NRCS (National Resources Conservation Service) in the United States Department of Agriculture. Important Farmland Map is biennially updated based on a computer mapping system, aerial imagery, public review, and field interpretation. NOTES: This data was reviewed by local jurisdictions and reflects each jurisdiction's input received during the SCAG's 2020 RTP/SCS Local Input and Envisioning Process.The updated Farmland categories are contained in 'polygon_ty' field. For more information, refer to the website at http://www.conservation.ca.gov/dlrp/fmmp/Pages/Index.aspx.PRIME FARMLAND (P)Farmland with the best combination of physical and chemical features able to sustain long term agricultural production. This land has the soil quality, growing season, and moisture supply needed to produce sustained high yields. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date.FARMLAND OF STATEWIDE IMPORTANCE (S)Farmland similar to Prime Farmland but with minor shortcomings, such as greater slopes or less ability to store soil moisture. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date.UNIQUE FARMLAND (U)Farmland of lesser quality soils used for the production of the state's leading agricultural crops. This land is usually irrigated, but may include non-irrigated orchards or vineyards as found in some climatic zones in California. Land must have been cropped at some time during the four years prior to the mapping date.FARMLAND OF LOCAL IMPORTANCE (L) Land of importance to the local agricultural economy as determined by each county's board of supervisors and a local advisory committee. GRAZING LAND (G)Land on which the existing vegetation is suited to the grazing of livestock. This category was developed in cooperation with the California Cattlemen's Association, University of California Cooperative Extension, and other groups interested in the extent of grazing activities. The minimum mapping unit for Grazing Land is 40 acres.URBAN AND BUILT-UP LAND (D)Land occupied by structures with a building density of at least 1 unit to 1.5 acres, or approximately 6 structures to a 10-acre parcel. This land is used for residential, industrial, commercial, institutional, public administrative purposes, railroad and other transportation yards, cemeteries, airports, golf courses, sanitary landfills, sewage treatment, water control structures, and other developed purposes.OTHER LAND (X)Land not included in any other mapping category. Common examples include low density rural developments; brush, timber, wetland, and riparian areas not suitable for livestock grazing; confined livestock, poultry or aquaculture facilities; strip mines, borrow pits; and water bodies smaller than 40 acres. Vacant and nonagricultural land surrounded on all sides by urban development and greater than 40 acres is mapped as Other Land.The Rural Land Mapping Project provides more detail on the distribution of various land uses within the Other Land category. The Rural Land categories include:Rural Residential Land (R), Semi-Agricultural and Rural Commercial Land (sAC), Vacant or Disturbed Land (V), Confined Animal Agriculture (Cl), and Nonagricultural or Natural Vegetation (nv).WATER (W)Perennial water bodies with an extent of at least 40 acres.NOT SURVEYED (Z)Large government land holdings, including National Parks, Forests, and Bureau of Land Management holdings are not included in FMMP’s survey area.

Cropland Index

The Cropland Index evaluates lands used to produce crops based on the following input datasets: Revised Storie Index, California Important Farmland data, Electrical Conductivity (EC), and Sodium Adsorption Ratio (SAR). Together, these input layers were used in a suitability model to generate this raster. High values are associated with better Croplands

Cartography is the knowledge associated with the art, science, and technology of maps. Maps portray spatial relationships among selected phenomena of interest and increasingly are used for analysis and synthesis. Through digital cartography and web mapping, however, it is possible for almost anyone to produce a bad map in minutes. Although cartography has undergone a radical transformation through the introduction of digital technology, fundamental principles remain. Doing computer cartography well requires a broad understanding of graphicacy as a language (as well as numeracy and literacy). This course provides an introduction to the principles, concepts, software, and hardware necessary to produce good maps, especially in the context (and limitations) of geographic information systems (GIS) and the web.

You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of cartography projects to reinforce the material. Lastly, you will complete term projects. Please see the sequencing document for our suggestions as to the order in which to work through the material. We have also provided PDF versions of the lectures with the notes included.